Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.

Protective effects of antibiotic resistant bacteria on susceptibles in biofilm: Influential factors, mechanism, and modeling

In environmental biofilms, antibiotic-resistant bacteria facilitate the persistence of susceptible counterparts under antibiotic stresses, contributing to increased community-level resistance. However, there is a lack of quantitative understanding of this protective effect and its influential factors, hindering accurate risk assessment of biofilm resistance in diverse environment. This study isolated an opportunistic Escherichia coli pathogen from soil, and engineered it with plasmids conferring antibiotic resistance. Protective effects of the ampicillin resistant strain (AmpR) on their susceptible counterparts (AmpS) were observed in ampicillin-stress colony biofilms. The concentration of ampicillin delineated protective effects into 3 zones: continuous protection (<1 MIC of AmpS), initial AmpS/R dependent (1-8 MIC of AmpS), and ineffective (>8 MIC of AmpS). Intriguingly, Zone 2 exhibited a surprising "less is more" phenomenon tuned by the initial AmpS/R ratio, where biofilm with an initially lower AmpR (1:50 vs 50:1) harbored 30-90 % more AmpR after 24 h growth under antibiotic stress. Compared to AmpS, AmpR displayed superiority in adhesion, antibiotic degradation, motility, and quorum sensing, allowing them to preferentially colonize biofilm edge and areas with higher ampicillin. An agent-based model incorporating protective effects successfully simulated tempo-spatial dynamics of AmpR and AmpS influenced by antibiotic stress and initial AmpS/R. This study provides a holistic view on the pervasive but poorly understood protective effects in biofilm, enabling development of better risk assessment and precisely targeted control strategies of biofilm resistance in diverse environment.

A novel approach to model the role of mobility suppression and vaccinations in containing epidemics in a network of cities

This paper presents a comprehensive agent-based model for the spread of an infection in a network of cities. Directional mobility is defined between each two cities and can take different values. The work examines the role that such mobility levels play in containing the infection with various vaccination coverage and age distributions. The results indicate that mobility reduction is sufficient to control the disease under all circumstances and full lockdowns are not a necessity. It has to be reduced to different ratios depending on the vaccination level and age distribution. A key finding is that increasing vaccination coverage above a certain level does not affect the mobility suppression level required to control the infection anymore for the cases of young population and heterogeneous age distributions. By investigating several migration and commuting patterns, it is found that shutting mobility in a few local places is favored against reducing mobility over the entire country network. In addition, commuting -and not migration-influences the spread level of the infection. The work offers an exclusive combined network-based and agent-based model that makes use of randomly generated mobility matrices.

Agent-based approaches for biological modeling in oncology: A literature review

CONTEXT

Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions.

METHODOLOGY

This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023.

RESULTS

Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.

Model-driven decision support: A community-based meta-implementation strategy to predict population impact

PURPOSE

Standard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo.

METHODS

We describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach.

RESULTS

Using two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption.

CONCLUSIONS

MDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it.

Coastal adaptation and migration dynamics under future shoreline changes

In this study, we present a novel modeling framework that provides a stylized representation of coastal adaptation and migration dynamics under sea level rise (SLR). We develop an agent-based model that simulates household and government agents adapting to shoreline change and increasing coastal flood risk. This model is coupled to a gravity-based model of migration to simulate coastward migration. Household characteristics are derived from local census data from 2015, and household decisions are calibrated based on empirical survey data on household adaptation in France. We integrate projections of shoreline retreat and flood inundation levels under two Representative Concentration Pathways (RCPs) and account for socioeconomic development under two Shared Socioeconomic Pathways (SSPs). The model is then applied to simulate coastal adaptation and migration between 2015 and 2080. Our results indicate that without coastal adaptation, SLR could drive the cumulative net outmigration of 13,100 up to as many as 21,700 coastal inhabitants between 2015 and 2080 under SSP2-RCP4.5 and SSP5-RCP8.5, respectively. This amounts to between 3.0 %-3.7 % of the coastal population residing in the 1/100-year flood zone in 2080 under a scenario of SLR. We find that SLR-induced migration is largely dependent on the adaptation strategies pursued by households and governments. Household implementation of floodproofing measures combined with beach renourishment reduces the projected SLR-induced migration by 31 %-36 % when compared to a migration under a scenario of no adaptation. A sensitivity analysis indicates that the effect of beach renourishment on SLR-induced migration largely depends on the level of coastal flood protection offered by sandy beaches. By explicitly modeling household behavior combined with governmental protection strategies under increasing coastal risks, the framework presented in this study allows for a comparison of climate change impacts on coastal communities under different adaptation strategies.

Modeling benefits and tradeoffs of green infrastructure: Evaluating and extending parsimonious models for neighborhood stormwater planning

Green infrastructure is often proposed to complement conventional urban stormwater management systems that are stressed by extreme storms and expanding impervious surfaces. Established hydrological and hydraulic models inform stormwater engineering but are time- and data-intensive or aspatial, rendering them inadequate for rapid exploration of solutions. Simple spreadsheet models support quick site plan assessments but cannot adequately represent spatial interactions beyond a site. The present study builds on the Landscape Green Infrastructure Design (L-GrID) Model, a process-based spatial model that enables rapid development and exploration of green infrastructure scenarios to mitigate neighborhood flooding. We first explored how well L-GrID could replicate flooding reports in a neighborhood in Chicago, Illinois, USA, to evaluate its potential for green infrastructure planning. Although not meant for prediction, L-GrID was able to replicate the flooding reported and helped identify strategies for flood control. Once evaluated for this neighborhood, we extended the model to include water quality through the representation of dispersion and settling mechanisms for two pollutant surrogates-total nitrogen and total suspended solids. With the extended model, Landscape Green Infrastructure Design Model-Water Quality (L-GrID-WQ), we examined benefits, costs, and tradeoffs for different green infrastructure strategies. Bioswales were slightly more effective than other green infrastructure types in reducing flooding extent and downstream runoff and pollution, through increased infiltration and settling capacity. Permeable pavers followed in effectiveness and are suggested where spatial constraints may limit the installation of bioswales. Although green infrastructure supports both flooding and pollution control, small tradeoffs between these functions emerged across spatial layouts: strategies based on only curb-cuts better controlled pollution, while layouts that followed the path of water flow better controlled flooding. By illuminating such tradeoffs, L-GrID-WQ can support green infrastructure planning that prioritizes unique concerns in different areas of a landscape.

Complex adaptive systems-based framework for modeling the health impacts of climate change

Introduction

Climate change is a global phenomenon with far-reaching consequences, and its impact on human health is a growing concern. The intricate interplay of various factors makes it challenging to accurately predict and understand the implications of climate change on human well-being. Conventional methodologies have limitations in comprehensively addressing the complexity and nonlinearity inherent in the relationships between climate change and health outcomes.

Objectives

The primary objective of this paper is to develop a robust theoretical framework that can effectively analyze and interpret the intricate web of variables influencing the human health impacts of climate change. By doing so, we aim to overcome the limitations of conventional approaches and provide a more nuanced understanding of the complex relationships involved. Furthermore, we seek to explore practical applications of this theoretical framework to enhance our ability to predict, mitigate, and adapt to the diverse health challenges posed by a changing climate.

Methods

Addressing the challenges outlined in the objectives, this study introduces the Complex Adaptive Systems (CAS) framework, acknowledging its significance in capturing the nuanced dynamics of health effects linked to climate change. The research utilizes a blend of field observations, expert interviews, key informant interviews, and an extensive literature review to shape the development of the CAS framework.

Results and discussion

The proposed CAS framework categorizes findings into six key sub-systems: ecological services, extreme weather, infectious diseases, food security, disaster risk management, and clinical public health. The study employs agent-based modeling, using causal loop diagrams (CLDs) tailored for each CAS sub-system. A set of identified variables is incorporated into predictive modeling to enhance the understanding of health outcomes within the CAS framework. Through a combination of theoretical development and practical application, this paper aspires to contribute valuable insights to the interdisciplinary field of climate change and health. Integrating agent-based modeling and CLDs enhances the predictive capabilities required for effective health outcome analysis in the context of climate change.

Conclusion

This paper serves as a valuable resource for policymakers, researchers, and public health professionals by employing a CAS framework to understand and assess the complex network of health impacts associated with climate change. It offers insights into effective strategies for safeguarding human health amidst current and future climate challenges.

COVID-19 transmission in a resource dependent community with heterogeneous populations: An agent-based modeling approach

Outbreaks of COVID-19 in crowded work locations led to mass infection events during the pandemic that stressed health capacity in rural communities. This led to disparate responses - either isolating and restricting workers to facilities and potentially amplifying spread between them, more intense community wide restrictions, or an acceptance of higher disease spread. An extreme case is the salmon fishery in Bristol Bay, Alaska, where fishermen, factory workers, and residents all interact during the summer fishing season. During the pandemic, policy measures were debated, including community mask mandates, restricting workers to their boats and factories, and even closing the valuable seasonal fishery. We develop an agent-based SIR model (ABM) to examine COVID-19 transmission in a resource-dependent community populated by distinct subgroups. The model includes a virus spreading within and between three heterogenous populations who interact with other members of their type in their home location, and with different types of agents when out in the community. We simulate various non-pharmaceutical interventions and vaccination rates across these groups. Results demonstrate the efficacy of non-pharmaceutical interventions and vaccinations, as well as tradeoffs between duration and intensity and tradeoffs between groups impacted by the outbreak. This ABM demonstrates the impact of public policy mechanisms on health outcomes in resource-dependent communities with distinct populations.

Design and simulation of a cross-regional collaborative recycling system for secondary resources: A case of lead-acid batteries

In emerging economies, a significant amount of secondary resources are recycled by the informal sector, which can seriously harm the environment. However, some previous studies of industry management policy design ignored geographical factors. This paper introduces Geographic Information Systems into an agent-based cross-regional recycling model, and employs lead-acid batteries as an example. The model quantitatively displays the evolution of recycling markets in 31 provinces in Mainland China. Results show that: (1) High subsidies can significantly increase the number of formal enterprises in the short term, but their effectiveness decreases when the proportion of government funds in subsidies is above 80% in the long run; (2) The number of illegal recycling enterprises increases by 294% in eight inland provinces (e.g., Ningxia, Xinjiang) when all funds are invested in supervision, but this number is quite small in subsidy policy scenarios; (3) In four eastern regions, including Beijing and Tianjin, the number of illegal recycling enterprises decreases by 84% if supervision is more favored than subsidy; (4) In the optimal case where spatiotemporal factors are considered in all 31 regions, illegal recycling enterprises and waste lead emissions can be reduced by 95.59% and 45.85% nationwide. Our proposed recycling model offers a detailed simulation of multiple regions and diverse stakeholders, and serves as a useful reference for targeted recovery policies. Governments in inland regions like Ningxia and Xinjiang should implement subsidy policies, while supervision policies should be implemented in developed regions like Beijing and Tianjin.

Impact of post-incarceration care engagement interventions on HIV transmission among young Black men who have sex with men and their sexual partners: an agent-based network modeling study

Background

Understanding the impact of incarceration on HIV transmission among Black men who have sex with men is important given their disproportionate representation among people experiencing incarceration and the potential impact of incarceration on social and sexual networks, employment, housing, and medical care. We developed an agent-based network model (ABNM) of 10,000 agents representing young Black men who have sex with men in the city of Chicago to examine the impact of varying degrees of post-incarceration care disruption and care engagement interventions following release from jail on HIV incidence.

Methods

Exponential random graph models were used to model network formation and dissolution dynamics, and network dynamics and HIV care continuum engagement were varied according to incarceration status. Hypothetical interventions to improve post-release engagement in HIV care for individuals with incarceration (e.g., enhanced case management, linkage to housing and employment services) were compared to a control scenario with no change in HIV care engagement after release.

Finding

HIV incidence at 10 years was 4.98 [95% simulation interval (SI): 4.87, 5.09 per 100 person-years (py)] in the model population overall; 5.58 (95% SI 5.38, 5.76 per 100 py) among those with history of incarceration, and 12.86 (95% SI 11.89, 13.73 per 100 py) among partners of agents recently released from incarceration. Sustained post-release HIV care for agents with HIV and experiencing recent incarceration resulted in a 46% reduction in HIV incidence among post-incarceration partners [incidence rate (IR) per 100 py = 5.72 (95% SI 5.19, 6.27) vs. 10.61 (95% SI 10.09, 11.24); incidence rate ratio (IRR) = 0.54; (95% SI 0.48, 0.60)] and a 19% reduction in HIV incidence in the population overall [(IR per 100 py = 3.89 (95% SI 3.81-3.99) vs. 4.83 (95% SI 4.73, 4.92); IRR = 0.81 (95% SI 0.78, 0.83)] compared to a scenario with no change in HIV care engagement from pre-to post-release.

Interpretation

Developing effective and scalable interventions to increase HIV care engagement among individuals experiencing recent incarceration and their sexual partners is needed to reduce HIV transmission among Black men who have sex with men.

Funding

This work was supported by the following grants from the National Institutes of Health: R01DA039934; P20 GM 130414; P30 AI 042853; P30MH058107; T32 DA 043469; U2C DA050098 and the California HIV/AIDS Research Program: OS17-LA-003; H21PC3466.

Modeling the impact of changing sexual behaviors with opposite-sex partners and STI testing among women and men ages 15-44 on STI diagnosis rates in the United States 2012-2019

Objective

To estimate the potential contributions of reported changes in frequency of penile-vaginal sex (PVS), condom use and STI screening to changes in gonorrhea and chlamydial diagnoses from 2012 to 2019.

Methods

An agent-based model of the heterosexual population in the U.S. simulated the STI epidemics. Baseline was calibrated to 2012 diagnosis rates, testing, condom use, and frequency of PVS. Counterfactuals used behaviors from the 2017-2019 NSFG, and we evaluated changes in diagnosis and incidence rates in 2019.

Results

Higher testing rates increased gonorrhea and chlamydia diagnosis by 14% and 13%, respectively, but did not reduce incidence. Declining frequency of PVS reduced the diagnosis rate for gonorrhea and chlamydia 6% and 3% respectively while reducing incidence by 10% and 9% respectively. Declining condom use had negligible impact on diagnosis and incidence.

Conclusion

Understanding how changing behavior drives STI incidence is essential to addressing the growing epidemics. Changes in testing and frequency of PVS likely contributed to some, but not all, of the changes in diagnoses. More research is needed to understand the context within which changing sexual behavior and testing are occurring.

Spatial transferability of an agent-based model to simulate Taenia solium control interventions

BACKGROUND

Models can be used to study and predict the impact of interventions aimed at controlling the spread of infectious agents, such as Taenia solium, a zoonotic parasite whose larval stage causes epilepsy and economic loss in many rural areas of the developing nations. To enhance the credibility of model estimates, calibration against observed data is necessary. However, this process may lead to a paradoxical dependence of model parameters on location-specific data, thus limiting the model's geographic transferability.

METHODS

In this study, we adopted a non-local model calibration approach to assess whether it can improve the spatial transferability of CystiAgent, our agent-based model of local-scale T. solium transmission. The calibration dataset for CystiAgent consisted of cross-sectional data on human taeniasis, pig cysticercosis and pig serology collected in eight villages in Northwest Peru. After calibration, the model was transferred to a second group of 21 destination villages in the same area without recalibrating its parameters. Model outputs were compared to pig serology data collected over a period of 2 years in the destination villages during a trial of T. solium control interventions, based on mass and spatially targeted human and pig treatments.

RESULTS

Considering the uncertainties associated with empirical data, the model produced simulated pre-intervention pig seroprevalences that were successfully validated against data collected in 81% of destination villages. Furthermore, the model outputs were able to reproduce validated pig seroincidence values in 76% of destination villages when compared to the data obtained after the interventions. The results demonstrate that the CystiAgent model, when calibrated using a non-local approach, can be successfully transferred without requiring additional calibration.

CONCLUSIONS

This feature allows the model to simulate both baseline pre-intervention transmission conditions and the outcomes of control interventions across villages that form geographically homogeneous regions, providing a basis for developing large-scale models representing T. solium transmission at a regional level.

Modeling versatile and dynamic anaerobic metabolism for PAOs/GAOs competition using agent-based model and verification via single cell Raman Micro-spectroscopy

Side-stream enhanced biological phosphorus removal process (S2EBPR) has been demonstrated to improve performance stability and offers a suite of advantages compared to conventional EBPR design. Design and optimization of S2EBPR require modification of the current EBPR models that were not able to fully reflect the metabolic functions of and competition between the polyphosphate-accumulating organisms (PAOs) and glycogen-accumulating organisms (GAOs) under extended anaerobic conditions as in the S2EBPR conditions. In this study, we proposed and validated an improved model (iEBPR) for simulating PAO and GAO competition that incorporated heterogeneity and versatility in PAO sequential polymer usage, staged maintenance-decay, and glycolysis-TCA pathway shifts. The iEBPR model was first calibrated against bulk batch testing experiment data and proved to perform better than the previous EBPR model for predicting the soluble orthoP, ammonia, biomass glycogen, and PHA temporal profiles in a starvation batch testing under prolonged anaerobic conditions. We further validated the model with another independent set of anaerobic testing data that included high-resolution single-cell and specific population level intracellular polymer measurements acquired with single-cell Raman micro-spectroscopy technique. The model accurately predicted the temporal changes in the intracellular polymers at cellular and population levels within PAOs and GAOs, and further confirmed the proposed mechanism of sequential polymer utilization, and polymer availability-dependent and staged maintenance-decay in PAOs. These results indicate that under extended anaerobic phases as in S2EBPR, the PAOs may gain competitive advantages over GAOs due to the possession of multiple intracellular polymers and the adaptive switching of the anaerobic metabolic pathways that consequently lead to the later and slower decay in PAOs than GAOs. The iEBPR model can be applied to facilitate and optimize the design and operations of S2EBPR for more reliable nutrient removal and recovery from wastewater.

Validating model output in the absence of ground truth data: A COVID-19 case study using the Simulator of Infectious Disease Dynamics in North Carolina (SIDD-NC) model

As the COVID-19 pandemic has progressed, various models have been developed to forecast changes in the outbreak and assess intervention strategies. In this study we validate the Simulator of Infectious Disease Dynamics in North Carolina (SIDD-NC) model against an ensemble of proxy-ground truth infections datasets. We assess the performance of SIDD-NC using Spearman Rank Correlation, RMSE, and percent RMSE at a state and county level. We conduct the analysis for the period of March 2020 through November 2020 as well as in shorter time increments to assess both the recreation of the pandemic curve as well as day-to-day transmission of SARS-CoV-2 within the population. We find that SIDD-NC performs well against the datasets in the ensemble, generating an estimate of infections that is robust both spatially and temporally.

The role of luck in the success of social media influencers

Motivation

Social media platforms centered around content creators (CCs) faced rapid growth in the past decade. Currently, millions of CCs make livable incomes through platforms such as YouTube, TikTok, and Instagram. As such, similarly to the job market, it is important to ensure the success and income (usually related to the follower counts) of CCs reflect the quality of their work. Since quality cannot be observed directly, two other factors govern the network-formation process: (a) the visibility of CCs (resulted from, e.g., recommender systems and moderation processes) and (b) the decision-making process of seekers (i.e., of users focused on finding CCs). Prior virtual experiments and empirical work seem contradictory regarding fairness: While the first suggests the expected number of followers of CCs reflects their quality, the second says that quality does not perfectly predict success.

Results

Our paper extends prior models in order to bridge this gap between theoretical and empirical work. We (a) define a parameterized recommendation process which allocates visibility based on popularity biases, (b) define two metrics of individual fairness (ex-ante and ex-post), and (c) define a metric for seeker satisfaction. Through an analytical approach we show our process is an absorbing Markov Chain where exploring only the most popular CCs leads to lower expected times to absorption but higher chances of unfairness for CCs. While increasing the exploration helps, doing so only guarantees fair outcomes for the highest (and lowest) quality CC. Simulations revealed that CCs and seekers prefer different algorithmic designs: CCs generally have higher chances of fairness with anti-popularity biased recommendation processes, while seekers are more satisfied with popularity-biased recommendations. Altogether, our results suggest that while the exploration of low-popularity CCs is needed to improve fairness, platforms might not have the incentive to do so and such interventions do not entirely prevent unfair outcomes.

Modeling Oldowan tool transport from a primate perspective

Living nonhuman primates have long served as a referential framework for understanding various aspects of hominin biological and cultural evolution. Comparing the cognitive, social, and ecological contexts of nonhuman primate and hominin tool use has allowed researchers to identify key adaptations relevant to the evolution of hominin behavior. Although the Oldowan is often considered to be a major evolutionary milestone, it has been argued that the Oldowan is rather an extension of behaviors already present in the ape lineage. This is based on the fact that while apes move tools through repeated, unplanned, short-distance transport bouts, they produce material patterning often associated with long-distance transport, planning, and foresight in the Oldowan. Nevertheless, remain fundamental differences in how Oldowan core and flake technology and nonhuman primate tools are used. The goal of the Oldowan hominins is to produce sharp-edged flakes, whereas nonhuman primates use stone tools primarily as percussors. Here, we present an agent-based model that investigates the explanatory power of the ape tool transport model in light of these differences. The model simulates the formation of the Oldowan record under the conditions of an accumulated short-distance transport pattern, as seen in extant chimpanzees. Our results show that while ape tool transport can account for some of the variation observed in the archaeological record, factors related to use-life duration severely limit how far an Oldowan core can be moved through repeated short-distance transport bouts. Thus, the ape tool transport has limitations in its ability to explain patterns in the Oldowan. These results provide a basis for discussing adaptive processes that would have facilitated the development of the Oldowan.

Effects of extreme events on land-use-related decisions of farmers in Eastern Austria: the role of learning

European farm households will face increasingly challenging conditions in the coming decades due to climate change, as the frequency and severity of extreme weather events rise. This study assesses the complex interrelations between external framework conditions such as climate change or adjustments in the agricultural price and subsidy schemes with farmers' decision-making. As social aspects remain understudied drivers for agricultural decisions, we also consider value-based characteristics of farmers as internal factors relevant for decision-making. We integrate individual learning as response to extreme weather events into an agent-based model that simulates farmers' decision-making. We applied the model to a region in Eastern Austria that already experiences water scarcity and increasing drought risk from climate change and simulated three future scenarios to compare the effects of changes in socio-economic and climatic conditions. In a cross-comparison, we then investigated how farmers can navigate these changes through individual adaptation. The agricultural trajectories project a decline of active farms between -27 and -37% accompanied by a reduction of agricultural area between -20 and -30% until 2053. The results show that regardless of the scenario conditions, adaptation through learning moderates the decline in the number of active farms and farmland compared to scenarios without adaptive learning. However, adaptation increases the workload of farmers. This highlights the need for labor support for farms.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13593-023-00890-z.

Evaluating the effects of policies on building construction waste management: a hybrid dynamic approach

The construction industry, as a vital pillar of a country's economy, generates a significant amount of construction waste, which places a tremendous burden on the environment and society. Although previous studies have explored the impact of policies on construction waste management, there is a lack of a simulation model that can be easily used, taking into account the dynamic nature, generality, and practicability of the model. To fill this gap, a hybrid dynamics model of construction waste management system is developed using agent-based modeling, system dynamics, perceived value, and experienced weighted attraction. Based on relevant data from the construction waste industry in Shenzhen, China, the effect of five policies on contractor strategy selection and overall evolution is tested. The results indicate that industry rectification policy and combination policy can effectively promote the resource treatment of construction waste and reduce illegal dumping, pollution to the environment of waste and treatment process, and waste treatment cost. The findings of this research will help not only researchers better analyze the effect of construction waste policies but also policymakers and practitioners in proposing effective construction waste management policies.

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility

The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. A major challenge in forecasting the transmission of COVID-19 is the accurate assessment of the multiscale human mobility and how it impacts infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of geographical places, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behavior and individual health conditions on the disease outbreak and the probability of zero-COVID in the population. Specifically, individuals perform power law-type local movements within a container and global transport between different-level containers. It is revealed that frequent long-distance movements inside a small-level container (e.g., a road or a county) and a small population size reduce both the local crowdedness and disease transmission. It takes only half of the time to induce global disease outbreaks when the population increases from 150 to 500 (normalized unit). When the exponent c 1 of the long-tail distribution of distance k moved in the same-level container, p ( k ) ∼ k - c 1 · level , increases, the outbreak time decreases rapidly from 75 to 25 (normalized unit). In contrast, travel between large-level containers (e.g., cities and nations) facilitates global spread of the disease and outbreak. When the mean traveling distance across containers 1 d increases from 0.5 to 1 (normalized unit), the outbreak occurs almost twice as fast. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or to a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing population size and restricting global travel help achieve zero-COVID-19. Specifically, when c 1 is smaller than 0.2, the ratio of people with low levels of mobility is larger than 80% and the population size is smaller than 400, zero-COVID can be achieved within fewer than 1000 time steps. In summary, the Mob-Cov model considers more realistic human mobility at a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to apply when investigating pandemic dynamics and when planning actions against disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11071-023-08489-5.

Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden

Introduction

Current influenza vaccines have limited effectiveness. COVID-19 vaccines using mRNA technology have demonstrated very high efficacy, suggesting that mRNA vaccines could be more effective for influenza. Several such influenza vaccines are in development. FRED, an agent-based modeling platform, was used to estimate the impact of more effective influenza vaccines on seasonal influenza burden.

Methods

Simulations were performed using an agent-based model of influenza that included varying levels of vaccination efficacy (40-95 % effective). In some simulations, level of infectiousness and/or length of infectious period in agents with breakthrough infections was also decreased. Impact of increased and decreased levels of vaccine uptake were also modeled. Outcomes included number of symptomatic influenza cases estimated for the US.

Results

Highly effective vaccines significantly reduced estimated influenza cases in the model. When vaccine efficacy was increased from 40 % to a maximum of 95 %, estimated influenza cases in the US decreased by 43 % to > 99 %. The base simulation (40 % efficacy) resulted in ∼ 28 million total yearly cases in the US, while the most effective vaccine modeled (95 % efficacy) decreased estimated cases to ∼ 22,000.

Discussion

Highly effective vaccines could dramatically reduce influenza burden. Model estimates suggest that even modest increases in vaccine efficacy could dramatically reduce seasonal influenza disease burden.

Agent-based simulation of COVID-19 containment measures: the case of lockdowns in cities

The effectiveness and political feasibility of COVID-19 containment measures such as lockdowns, are contentious. This stems in part from an absence of tools for their rigorous evaluation. Common epidemiological models such as the SEIR model generally lack the spatial resolution required for micro-level containment actions, the visualization capabilities for communicating measures such as localized lockdowns and the scenario-testing capabilities for assessing different alternatives. We present an individual-level ABM that generates geo-social networks animated by agent-agent and agent-building interactions. The model simulates real-world contexts and is demonstrated for the city of Jerusalem. Simulation outputs yield much useful information for evaluating the effectiveness of lockdowns. These include network-generated socio-spatial contagion chains for individual agents, dynamic building level contagion processes and neighborhood-level patterns of COVID-19 imports and exports useful in identifying super-spreader neighborhoods. The policy implications afforded by these various outputs are discussed.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12076-023-00336-w.

Optimizing the maintenance strategies for a network of green infrastructure: An agent-based model for stormwater detention basins

Various stormwater best management practices and green infrastructures (GIs) are recommended to address flooding, stormwater runoff, water quality, and sustainability. While detention basins are considered one of the main GI strategies, their benefits cannot be fully realized without properly maintaining them and making sure that they stay operational. Therefore, this paper used agent-based modeling (ABM) to devise an optimal maintenance program for detention basins to ensure that they function properly and continue to perform their water quality and flood control functions. More specifically, the following 2 agent types were incorporated in the model: 1) the detention basins were considered as static agents, and 2) the service teams responsible for the operation (maintenance, repair, and replacement) of the detention basins were considered as active agents. The developed ABM was applied for the entire network of stormwater detention basins in Newark, NJ. Sensitivity analysis was conducted to identify the most critical variables affecting the total cost of operating the network of detention basins as well as the functioning percentage of detention basins. In addition, optimization was implemented to determine the best maintenance program or policy that minimizes the total cost of operations, while also making sure that a desired functionality level or threshold is achieved for the entire network of detention basins. Finally, the ABM was statistically validated using a total of 10,000 Monte Carlo runs and 99% confidence intervals. The optimization results showed that, in order to minimize the total cost of maintaining the entire network of detention basins and ensure that at least 80% of the basins are in a functioning state at the end of the planning horizon, the decision-maker should implement the following maintenance program or strategy: have 2 service teams for the operations of the detention basins, follow a replacement policy, and replace detention basins after 3 maintenance periods. Also, the identified optimal maintenance program or strategy would result with an average total annual cost of around $4,085,000, where the average annual repair cost is around $2,572,200, the average annual maintenance cost is around $19,700, the average annual replacement cost is around $763,100, and the average annual service team cost is around $730,000. The proposed ABM for detention basins can be extended to other GIs as well as to different geographical areas. The usage of ABM has the advantage to reduce the subjectivity in developing plans for managing GIs.

Simulation of SARS-CoV-2 epidemic trends in Tokyo considering vaccinations, virus mutations, government policies and PCR tests

The eighth wave of COVID-19 infection in the Tokyo area has brought daily confirmed cases to a new higher level. This paper aims to explain the previous seven epidemic waves and forecast the eighth epidemic trend of the area using agent-based modeling and extended SEIR denotation. Four key considerations are investigated in this research, that are: 1. Vaccination, 2. Virus mutations, 3. Governmental policies and 4. PCR tests. Our study finds that the confirmed cases in the previous seven epidemic waves were only the tip of the iceberg. Using data prior to December 1 2022, the eighth wave is expected to hover high in December 2022 and January 2023. Our research pioneers in the simulation of antibody declination on an individual level. Comparing the simulated results, we find that the arrival of new epidemic waves are related to the decline in the number of antibody possessors, especially the sixth and the seventh epidemic waves. Our simulation also suggests that faced with low severe and death rates, PCR tests would not make much difference to reduce overall infections. In this case, maintaining PCR tests to a low level helps to reduce both social cost and public anxiety. However, if faced with the opposite case, PCR tests should be adjusted to a higher level to detect early infections. Such level of PCR tests should be compatible with available medical resources.

What's stopping the waste-treatment industry from adopting emerging circular technologies? An agent-based model revealing drivers and barriers

Many new circular economy technologies are gaining momentum, yet research on the complexity of adoption decisions driven by uncertainties, both at technology and ecosystem level, is lacking. In the present study, an agent-based model was developed to study factors that influence the adoption of emerging circular technologies. The case of the waste treatment industry was chosen, specifically its (non-) adoption of the so-called "Volatile Fatty Acid Platform", a circular economy technology that facilitates both the valorization of organic waste into high-end products as well as their sale on global markets. Model results show adoption rates under 60% due to effects of subsidies, market growth, technological uncertainty and social pressure. Furthermore, the conditions were revealed under which certain parameters have the most effect. An agent-based model enabled use of a systemic approach to reveal the mechanisms of circular emerging technology innovation that are most relevant for researchers and waste treatment stakeholders.

An integrated socio-economic agent-based modeling framework towards assessing farmers' decision making under water scarcity and varying utility functions

A spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers' decisions concerning their future farming practices when faced with potential water scarcity induced by future climate change. The proposed framework forecasts farmers' behavior assuming varying utility functions. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline. The model results indicated that modelling farmers as agents, who were solely interested in optimizing their agro-business budget, was only able to reproduce 35% of the answers provided by the farmers through a administered field questionnaire. Model simulations highlighted the importance of representing the farmers' combined socio-economic attributes when assessing their future decisions on land tenure. This approach accounts for social factors, such as the farmers' attitudes, subjective norms, social influence, memories of previous civil unrest and farming traditions, in addition to their economic utility to model farmer decision making. Under this scenario, correspondence between model simulations and farmers' answers reached 95%. Additionally, the model results show that when faced with the negative impacts of climate change, the majority of farmers seek adaptive measures, such as changing their crops and/or seeking new water sources, only when future water shortages were predicted to be low to moderate. Most opt to cease farming and allow their lands to urbanize or go fallow, when future water shortages were predicted to be high. Meanwhile, incorporating and modeling the social influence structures within the ABM diminished farmers' willingness to adapt and doubled their propensity to sell or quit their land. The proposed framework is able to account for a variety of utility functions and to successfully capture the actions and interactions between farmers and their environment; thus, it represents an innovative modeling approach for assessing farmers' behavior and decision-making in the face of future climate change. The nonspecific structure of the framework allows its application at any agriculturally dominated setting facing future water shortages promulgated by a changing climate.

Testing the Social Function of Metacognition for Common-Pool Resource Use

Metacognition, the ability to monitor and evaluate our own cognitive processes, confers advantages to individuals and their own judgment. A more recent hypothesis, however, states that explicit metacognition may also enhance the collective judgment of groups, and may enhance human collaboration and coordination. Here, we investigate this social function hypothesis of metacognition with arguably one of the oldest collaboration problems humans face, common-pool resource use. Using an agent-based model that simulates repeated group interactions and the forming of collective judgments about resource extraction, we show that (1) in "kind" environments (where confidence and judgment accuracy correlate positively), explicit metacognition may allow groups to reach more accurate judgments compared to groups exchanging object-level information only; while (2) in "wicked" environments (where confidence and judgment accuracy correlate negatively), explicit metacognition may protect groups from the large judgment errors yielded by groups using metacognitive information for individual-level learning only (implicit metacognition). With explicit metacognition, this research highlights a novel mechanism which, among others, provides a testable explanation of the often-observed finding that groups all over the world communicate to enhance common property use.

Impact of changes in protective behaviors and out-of-household activities by age on COVID-19 transmission and hospitalization in Chicago, Illinois

PURPOSE

Even with an efficacious vaccine, protective behaviors (social distancing, masking) are essential for preventing COVID-19 transmission and could become even more important if current or future variants evade immunity from vaccines or prior infection.

METHODS

We created an agent-based model representing the Chicago population and conducted experiments to determine the effects of varying adult out-of-household activities (OOHA), school reopening, and protective behaviors across age groups on COVID-19 transmission and hospitalizations.

RESULTS

From September-November 2020, decreasing adult protective behaviors and increasing adult OOHA both substantially impacted COVID-19 outcomes; school reopening had relatively little impact when adult protective behaviors and OOHA were maintained. As of November 1, 2020, a 50% reduction in young adult (age 18-40) protective behaviors resulted in increased latent infection prevalence per 100,000 from 15.93 (IQR 6.18, 36.23) to 40.06 (IQR 14.65, 85.21) and 19.87 (IQR 6.83, 46.83) to 47.74 (IQR 18.89, 118.77) with 15% and 45% school reopening. Increasing adult (age ≥18) OOHA from 65% to 80% of prepandemic levels resulted in increased latent infection prevalence per 100,000 from 35.18 (IQR 13.59, 75.00) to 69.84 (IQR 33.27, 145.89) and 38.17 (IQR 15.84, 91.16) to 80.02 (IQR 30.91, 186.63) with 15% and 45% school reopening. Similar patterns were observed for hospitalizations.

CONCLUSIONS

In areas without widespread vaccination coverage, interventions to maintain adherence to protective behaviors, particularly among younger adults and in out-of-household settings, remain a priority for preventing COVID-19 transmission.

How to design virus containment policies? A joint analysis of economic and epidemic dynamics under the COVID-19 pandemic

We analyze the impact of different designs of COVID-19-related lockdown policies on economic loss and mortality using a micro-level simulation model, which combines a multi-sectoral closed economy with an epidemic transmission model. In particular, the model captures explicitly the (stochastic) effect of interactions between heterogeneous agents during different economic activities on virus transmissions. The empirical validity of the model is established using data on economic and pandemic dynamics in Germany in the first 6 months after the COVID-19 outbreak. We show that a policy-inducing switch between a strict lockdown and a full opening-up of economic activity based on a high incidence threshold is strictly dominated by alternative policies, which are based on a low incidence threshold combined with a light lockdown with weak restrictions of economic activity or even a continuous weak lockdown. Furthermore, also the ex ante variance of the economic loss suffered during the pandemic is substantially lower under these policies. Keeping the other policy parameters fixed, a variation of the consumption restrictions during the lockdown induces a trade-off between GDP loss and mortality. Furthermore, we study the robustness of these findings with respect to alternative pandemic scenarios and examine the optimal timing of lifting containment measures in light of a vaccination rollout in the population.

Pharmacokinetics and Pharmacodynamics of TAK-164 Antibody Drug Conjugate Coadministered with Unconjugated Antibody

The development of new antibody-drug conjugates (ADCs) has led to the approval of 7 ADCs by the FDA in 4 years. Given the impact of intratumoral distribution on efficacy of these therapeutics, coadministration of unconjugated antibody with ADC has been shown to improve distribution and efficacy of several ADCs in high and moderately expressed tumor target systems by increasing tissue penetration. However, the benefit of coadministration in low expression systems is less clear. TAK-164, an ADC composed of an anti-GCC antibody (5F9) conjugated to a DGN549 payload, has demonstrated heterogeneous distribution and bystander killing. Here, we evaluated the impact of 5F9 coadministration on distribution and efficacy of TAK-164 in a primary human tumor xenograft mouse model. Coadministration was found to improve the distribution of TAK-164 within the tumor, but it had no significant impact (increase or decrease) on efficacy. Experimental and computational evidence indicates that this was not a result of tumor saturation, increased binding to perivascular cells, or compensatory bystander effects. Rather, the cellular potency of DGN549 was matched with the single-cell uptake of TAK-164 making its IC50 close to its equilibrium binding affinity (KD), and as such, coadministration dilutes total DGN549 in cells below the maximum cytotoxic concentration, thereby offsetting an increased number of targeted cells with decreased ability to kill each cell. These results provide new insights on matching payload potency to ADC delivery to help identify when increasing tumor penetration is beneficial for improving ADC efficacy and demonstrate how mechanistic simulations can be leveraged to design clinically effective ADCs.

It's worth a shot: urban density, endogenous vaccination decisions, and dynamics of infectious disease

We develop an agent-based model of vaccine decisions across a heterogeneous network model with urban and rural regions. In the model, agents make rational decisions to vaccinate or not, based on the relative private costs of vaccinations and infections as well as an estimated probability of infection if not vaccinated. The model is a methodological advance in that it provides an economic rationale for traditional threshold models of vaccine decision-making that are commonly used in agent-based network models of vaccine choice. In the model, more dense urban regions have more connections between agents than less dense rural regions. Higher density leads to higher levels of vaccine usage and lower rates of infection in urban regions within the model. This finding adds to the more commonly discussed socio-economic reasons for higher levels of vaccination usage in urban areas compared to rural areas. In addition to this direct contribution, the paper emphasizes the importance of endogenous decision-making in models of epidemiology. For instance, we find that networks that lead to larger epidemics in exogenous vaccination models lead to smaller epidemics in our model because agents use vaccinations to offset the additional risk introduced by these network structures. Endogenous agent responses to risk need to be incorporated into theoretical and empirical models of economic epidemiology.

The Emergence of Cultural Attractors: How Dynamic Populations of Learners Achieve Collective Cognitive Alignment

When a population exhibits collective cognitive alignment, such that group members tend to perceive, remember, and reproduce information in similar ways, the features of socially transmitted variants (i.e., artifacts, behaviors) may converge over time towards culture-specific equilibria points, often called cultural attractors. Because cognition may be plastic, shaped through experience with the cultural products of others, collective cognitive alignment and stable cultural attractors cannot always be taken for granted, but little is known about how these patterns first emerge and stabilize in initially uncoordinated populations. We propose that stable cultural attractors can emerge from general principles of human categorization and communication. We present a model of cultural attractor dynamics, which extends a model of unsupervised category learning in individuals to a multiagent setting wherein learners provide the training input to each other. Agents in our populations spontaneously align their cognitive category structures, producing emergent cultural attractor points. We highlight three interesting behaviors exhibited by our model: (1) noise enhances the stability of cultural category structures; (2) short 'critical' periods of learning early in life enhance stability; and (3) larger populations produce more stable but less complex attractor landscapes, and cliquish network structure can mitigate the latter effect. These results may shed light on how collective cognitive alignment is achieved in the absence of shared, innate cognitive attractors, which we suggest is important to the capacity for cumulative cultural evolution.

Network effects in a bounded confidence model

The bounded confidence model has become a popular tool for studying communities of epistemically interacting agents. The model makes the idealizing assumption that all agents always have access to all other agents' belief states. We draw on resources from network epistemology to do away with this assumption. In the model to be proposed, we impose an explicit communication network on a community, due to which each agent has access to the beliefs of only a selection of other agents. A much-discussed result from network epistemology shows that densely connected communication networks are not always preferable to sparser networks. The aim of this paper is to investigate whether there are any noteworthy network effects in a version of the bounded confidence model augmented with communication networks, and in particular whether the aforementioned result from network epistemology can be replicated in that version.

The casual effect of relational mobility on integration of social networks: An agent-based modeling approach

Despite converging evidence for the importance of relational mobility on shaping people's social experiences, previous work suggested mixed findings for its influence on the structure of sociocentric networks, which lays the basis for the development of all types of social relationships. Additionally, as it is timely and economically intractable to administer such longitudinal experiments in real-life settings, most previous work mainly relied on cross-sectional correlation analyses and provided limited causal evidence. The current research used an agent-based modeling approach to examine whether higher relational mobility (i.e., the number of opportunities to meet new people) would promote integration among social networks over time. Using parameters derived from survey data, we simulated how the integration of sociocentric social networks evolves under different levels of relational mobility. Based on the data of three network structural indicators, including modularity, global efficiency, and standard deviation of nodal betweenness, we obtained causal evidence supporting that higher relational mobility promotes greater network integration. These findings highlight the power of socioecological demands on our social experiences.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12144-022-03130-x.

Two sides of the same leader: an agent-based model to analyze the effect of ambivalent opinion leaders in social networks

Opinion leaders (OLs) are becoming increasingly relevant on social networking sites as their visibility can help to shape their followers' attitudes toward a variety of issues. While earlier research provided initial evidence on the effect of OLs using agent-based modeling, it remains unclear how OLs affect their network environment and, therefore, the opinion climate when: (a) they publicly hold ambivalent attitudes, and (b) they not only express support for their own stance but also discredit or 'debunk' the opposing side. This paper presents an agent-based model that determines the influence of OLs in social networks in relation to ambivalence and discreditation. The model draws on theoretical foundations of OLs as well as attitudinal ambivalence and was implemented using two network topologies. Results indicate that OLs have significant influence on the opinion climate and that an unequal number of OLs of different opinion camps lead to an imbalance in the opinion climate only in certain situations. Furthermore, OLs can dominate the opinion climate and turn their stance into a majority opinion more effectively when discrediting the opposing side. Ambivalent OLs, on the other hand, can contribute to greater balance in the opinion climate. These findings provide a more nuanced analysis of OLs in social networks by pointing to potential amplifications as well as boundaries of their influence. Implications are discussed with a focus on human and artificial key actors in online networks and their efficacy therein.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s42001-022-00161-z.

A review of mathematical modeling of addiction regarding both (neuro-) psychological processes and the social contagion perspectives

Addiction is a complex biopsychosocial phenomenon, impacted by biological predispositions, psychological processes, and the social environment. Using mathematical and computational models that allow for surrogative reasoning may be a promising avenue for gaining a deeper understanding of this complex behavior. This paper reviews and classifies a selection of formal models of addiction focusing on the intra- and inter-individual dynamics, i.e., (neuro) psychological models and social models. We find that these modeling approaches to addiction are too disjoint and argue that in order to unravel the complexities of biopsychosocial processes of addiction, models should integrate intra- and inter-individual factors.

Agent-based modeling of COVID-19 outbreaks for New York state and UK: Parameter identification algorithm

This paper uses Covasim, an agent-based model (ABM) of COVID-19, to evaluate and scenarios of epidemic spread in New York State (USA) and the UK. Epidemiological parameters such as contagiousness (virus transmission rate), initial number of infected people, and probability of being tested depend on the region's demographic and geographical features, the containment measures introduced; they are calibrated to data about COVID-19 spread in the region of interest. At the first stage of our study, epidemiological data (numbers of people tested, diagnoses, critical cases, hospitalizations, and deaths) for each of the mentioned regions were analyzed. The data were characterized in terms of seasonality, stationarity, and dependency spaces, and were extrapolated using machine learning techniques to specify unknown epidemiological parameters of the model. At the second stage, the Optuna optimizer based on the tree Parzen estimation method for objective function minimization was applied to determine the model's unknown parameters. The model was validated with the historical data of 2020. The modeled results of COVID-19 spread in New York State and the UK have demonstrated that if the level of testing and containment measures is preserved, the number of positive cases in New York State remain the same during March of 2021, while in the UK it will reduce.

Different roles of interpersonal trust and institutional trust in COVID-19 pandemic control

The absence of pharmaceutical interventions made it particularly difficult to mitigate the first outbreak of coronavirus disease 2019 (COVID-19). The current study investigated how interpersonal trust and institutional trust influenced the control process. Trusts and COVID-19 data in 44 countries and 50 US states were analyzed; institutional trust was associated with case fatality rate, and interpersonal trust was associated with control speed. Two independent behavioral experiments showed that institutional trust manipulation increased participants’ willingness to complete the COVID-19 test and that interpersonal trust manipulation increased conscious compliance with prevention norms and decreased unnecessary outdoor activities. Agent-based modeling further confirmed these behavioral mechanisms for two types of trust in the COVID-19 control process. New interventions are needed to help countries heighten interpersonal and institutional trust as they continue to battle COVID-19 and other collective threats.

ArchABM: An agent-based simulator of human interaction with the built environment. CO2 and viral load analysis for indoor air quality

Recent evidence suggests that SARS-CoV-2, which is the virus causing a global pandemic in 2020, is predominantly transmitted via airborne aerosols in indoor environments. This calls for novel strategies when assessing and controlling a building's indoor air quality (IAQ). IAQ can generally be controlled by ventilation and/or policies to regulate human-building-interaction. However, in a building, occupants use rooms in different ways, and it may not be obvious which measure or combination of measures leads to a cost- and energy-effective solution ensuring good IAQ across the entire building. Therefore, in this article, we introduce a novel agent-based simulator, ArchABM, designed to assist in creating new or adapt existing buildings by estimating adequate room sizes, ventilation parameters and testing the effect of policies while taking into account IAQ as a result of complex human-building interaction patterns. A recently published aerosol model was adapted to calculate time-dependent carbon dioxide (CO2) and virus quanta concentrations in each room and inhaled CO2 and virus quanta for each occupant over a day as a measure of physiological response. ArchABM is flexible regarding the aerosol model and the building layout due to its modular architecture, which allows implementing further models, any number and size of rooms, agents, and actions reflecting human-building interaction patterns. We present a use case based on a real floor plan and working schedules adopted in our research center. This study demonstrates how advanced simulation tools can contribute to improving IAQ across a building, thereby ensuring a healthy indoor environment.

Tonal Emergence: An agent-based model of tonal coordination

Humans have a remarkable capacity for coordination. Our ability to interact and act jointly in groups is crucial to our success as a species. Joint Action (JA) research has often concerned itself with simplistic behaviors in highly constrained laboratory tasks. But there has been a growing interest in understanding complex coordination in more open-ended contexts. In this regard, collective music improvisation has emerged as a fascinating model domain for studying basic JA mechanisms in an unconstrained and highly sophisticated setting. A number of empirical studies have begun to elucidate coordination mechanisms underlying joint musical improvisation, but these findings have yet to be cached out in a working computational model. The present work fills this gap by presenting Tonal Emergence, an idealized agent-based model of improvised musical coordination. Tonal Emergence models the coordination of notes played by improvisers to generate harmony (i.e., tonality), by simulating agents that stochastically generate notes biased towards maximizing harmonic consonance given their partner's previous notes. The model replicates an interesting empirical result from a previous study of professional jazz pianists: feedback loops of mutual adaptation between interacting agents support the production of consonant harmony. The model is further explored to show how complex tonal dynamics, such as the production and dissolution of stable tonal centers, are supported by agents that are characterized by (i) a tendency to strive toward consonance, (ii) stochasticity, and (iii) a limited memory for previously played notes. Tonal Emergence thus provides a grounded computational model to simulate and probe the coordination mechanisms underpinning one of the more remarkable feats of human cognition: collective music improvisation.

An Agent-Based Model for Tertiary Educational Choices in Italy

Although the low level of tuition fees and the absence of other access barriers, Italy is characterized by low educational attainments at the university level. This work models the choice of young Italians to attend university or leave education and enter the labor market, by making use of an agent-based model that reproduces the Italian higher education and policy system. The aim is to analyze the determinants behind university enrollment decisions possibly causing the low level of attainment and explore three alternative scenarios that propose the expansion of financial support and the increase in the average income gap between skilled and unskilled individuals. The model implies that the individual preference to enroll at university depends upon (i) economic motivations, represented by the expectations on future income, which are formed through interaction within individuals' social network; (ii) influence from peers; (iii) effort of obtaining a university degree. Results show that the model can reproduce observable features of the Italian system, and highlights low income levels and the following full resort to regional scholarships. Experimented scenarios show that policies expanding financial support to education are ineffective, while an increase in the gap between average income of skilled and unskilled workers leads to an increase in enrollment in university, signaling that labor market policies may be more effective than educational policies in raising the number of students in higher education.

Multi-hazard hospital evacuation planning during disease outbreaks using agent-based modeling

As different types of hazards, including natural and man-made, can occur simultaneously, to implement an integrated and holistic risk management, a multi-hazard perspective on disaster risk management, including preparedness and planning, must be taken for a safer and more resilient society. Considering the emerging challenges that the COVID-19 pandemic has been introducing to regular hospital operations, there is a need to adapt emergency plans with the changing conditions, as well. Evacuation of patients with different mobility disabilities is a complicated process that needs planning, training, and efficient decision-making. These protocols need to be revisited for multi-hazard scenarios such as an ongoing disease outbreak during which additional infection control protocols might be in place to prevent transmission. Computational models can provide insights on optimal emergency evacuation strategies, such as the location of isolation units or alternative evacuation prioritization strategies. This study introduces a non-ICU patient classification framework developed based on available patient mobility data. An agent-based model was developed to simulate the evacuation of the emergency department at the Johns Hopkins Hospital during the COVID-19 pandemic due to a fire emergency. The results show a larger nursing team can reduce the median and upper bound of the 95% confidence interval of the evacuation time by 36% and 33%, respectively. A dedicated exit door for COVID-19 patients is relatively less effective in reducing the median time, while it can reduce the upper bound by more than 50%.

Urban life: a model of people and places

We introduce the Urban Life agent-based simulation used by the Ground Truth program to capture the innate needs of a human-like population and explore how such needs shape social constructs such as friendship and wealth. Urban Life is a spatially explicit model to explore how urban form impacts agents' daily patterns of life. By meeting up at places agents form social networks, which in turn affect the places the agents visit. In our model, location and co-location affect all levels of decision making as agents prefer to visit nearby places. Co-location is necessary (but not sufficient) to connect agents in the social network. The Urban Life model was used in the Ground Truth program as a virtual world testbed to produce data in a setting in which the underlying ground truth was explicitly known. Data was provided to research teams to test and validate Human Domain research methods to an extent previously impossible. This paper summarizes our Urban Life model's design and simulation along with a description of how it was used to test the ability of Human Domain research teams to predict future states and to prescribe changes to the simulation to achieve desired outcomes in our simulated world.

Towards an agent-based model to simulate osseointegration in powder-bed 3D printed implant-like structures

The orthopedic industry is still searching for an efficient way to replace bone loss due to surgical procedures such as arthroplasty and limb-sparing surgery. Additive manufacturing (AM) presents an opportunity to manufacture affordable patient-specific implants. Optimization of the implant-bone interface to maximize osseointegration (bone ingrowth) has not been appropriately addressed. Mechanobiological models, suited to predict mechanical adaptation of bone, cannot be used to predict osseointegration inside implants as the implant is not exposed to any mechanical loading until it is fully accepted by the host body. Biological models relying on partial differential equations based on continuum approximation are not well-suited to predict the discrete phenomenon of osseointegration. This study proposes an agent-based modeling (ABM) approach for representing the osseointegration process for orthopedic implants produced by powder-bed additive manufacturing processes. Agent-Based Modeling (ABM) is a cellular automata based discrete computing technique that uses rule-based mathematics derived from experimental studies to simulate evolutionary phenomena. In this paper, osseointegration inside a hexagonal closed packing of AM powder particles is modeled using ABM. Cellular agents such as pre-osteoblasts and osteoblasts are realistically modeled as cubic cells. The proposed model underpredicts osseointegration at early stages but predicts osseointegration at around 21 days with sufficient accuracy when compared to the in vitro test conducted by Xue et al. in 2007.

Memory transmission in small groups and large networks: An empirical study

When people try to remember information in a group, they often recall less than if they were recalling alone. This finding is called collaborative inhibition, and has been studied primarily in small groups because of the difficulty of bringing large groups into the laboratory. To study the dynamics of collaborative inhibition in large groups (Luhmann & Rajaram, Psychological Science, 26, 1909-1917, 2015) constructed an agent-based model that extrapolated from previous laboratory experiments with small groups. The model predicts that collaborative inhibition should increase with group size. Here, we evaluate this model by recruiting a large number of participants using crowdsourcing, allowing us to replace the artificial agents in the model with people to study collaborative memory at larger scales. Our empirical results did not match the model predictions: there was no evidence for an increase in collaborative inhibition with group size, despite substantial power to detect such an effect. These findings motivate further empirical work to elucidate the mechanisms of collaborative memory.

An Agent-based Decision Support for a Vaccination Campaign

We explore the Covid-19 diffusion with an agent-based model of an Italian region with a population on a scale of 1:1000. We also simulate different vaccination strategies. From a decision support system perspective, we investigate the adoption of artificial intelligence techniques to provide suggestions about more effective policies. We adopt the widely used multi-agent programmable modeling environment NetLogo, adding genetic algorithms to evolve the best vaccination criteria. The results suggest a promising methodology for defining vaccine rates by population types over time. The results are encouraging towards a more extensive application of agent-oriented methods in public healthcare policies.

Prediction and validation of avascular tumor growth pattern in different metabolic conditions using in silico and in vitro models

Objectives: In recent years, scientists have taken many efforts for in vitro and in silico modeling of cancerous tumors. In fact, three-dimensional (3D) cultures of multicellular tumor spheroids (MCTSs) are good validators for computational results. The goal of this study is to simulate the 3D early growth of avascular tumors using MCTSs and to compare the in vitro models with the results and predictions of a specific computational modeling framework. Using these two types of models, the importance of metabolic condition on tumor growth behavior and necrosis could be predicted. Materials and methods: We took advantage of a previously developed computational model of tumor growth (constructed by integrating a generic metabolic network model of cancer cells with a multiscale agent-based framework). Among the computational predictions is the importance of glucose accessibility on tumor growth behavior. To study the effect of glucose concentration experimentally, MCTSs were grown in high and low glucose culture media. After that, tumor growth pattern was analyzed by MTT assay, cell counting and propidium iodide (PI) staining. Results: We obviously observed that the rate of necrosis increases and the rate of tumor growth and cell activity decreases as the glucose availability reduces, which is in line with the computational model prediction.

Big data-drive agent-based modeling of online polarized opinions

Under the mobile internet and big data era, more and more people are discussing and interacting online with each other. The forming process and evolutionary dynamics of public opinions online have been heavily investigated. Using agent-based modeling, we expand the Ising model to explore how individuals behave and the evolutionary mechanism of the life cycles. The big data platform of Douban.com is selected as the data source, and the online case “NeiYuanWaiFang” is applied as the real target, for our modeling and simulations to match. We run 10,000 simulations to find possible optimal solutions, and we run 10,000 times again to check the robustness and adaptability. The optimal solution simulations can reflect the whole life cycle process. In terms of different levels and indicators, the fitting or matching degrees achieve the highest levels. At the micro-level, the distributions of individual behaviors under real case and simulations are similar to each other, and they all follow normal distributions; at the middle-level, both discrete and continuous distributions of supportive and oppositive online comments are matched between real case and simulations; at the macro-level, the life cycle process (outbreak, rising, peak, and vanish) and durations can be well matched. Therefore, our model has properly seized the core mechanism of individual behaviors, and precisely simulated the evolutionary dynamics of online cases in reality.

Early warning signals from the periphery: A model suggestion for the study of critical transitions

Studies on the possibility of predicting critical transitions with statistical methods known as early warning signals (EWS) are often conducted on data generated with equation-based models (EBMs). These models base on difference or differential equations, which aggregate a system's components in a mathematical term and therefore do not allow for a detailed analysis of interactions on micro-level. As an alternative, we suggest a simple, but highly flexible agent-based model (ABM), which, when applying EWS-analysis, gives reason to (a) consider social interaction, in particular negative feedback effects, as an essential trigger of critical transitions, and (b) to differentiate social interactions, for example in network representations, into a core and a periphery of agents and focus attention on the periphery. Results are tested against time series from a networked version of the Ising-model, which is often used as example for generating hysteretic critical transitions.